2019/08/19 by Shirin Seyedsalehi, Vahid Pourahmadi, Seyedsalehi, Shirin +5 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Digital Media Forensic Detection #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.06767
openalex publication_date 2019/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Channel, as the medium for the propagation of electromagnetic waves, is one of the most important parts of a communication system. Being aware of how the channel affects the propagation waves is essential for designing, optimization and performance analysis of a communication system. For this purpose, a proper channel model is needed. This paper presents a novel propagation channel model which considers the time-frequency response of the channel as an image. It models the distribution of these channel images using Deep Convolutional Generative Adversarial Networks. Moreover, for the measurements with different user speeds, the user speed is considered as an auxiliary parameter for the model. StarGAN as an image-to-image translation technique is used to change the generated channel images with respect to the desired user speed. The performance of the proposed model is evaluated using existing metrics. Furthermore, to capture 2D similarity in both time and frequency, a new metric is introduced. Using this metric, the generated channels show significant statistical similarity to the measurement data.